Triple
T8309987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region of Southern Denmark |
E194566
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Assens
Assens is a coastal town on the island of Funen in southern Denmark, known for its historic harbor, maritime heritage, and well-preserved old town.
|
E725077
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Assens | Statement: [Region of Southern Denmark, containsCity, Assens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assens Context triple: [Region of Southern Denmark, containsCity, Assens]
-
A.
Åsnes
Åsnes is a rural municipality in Innlandet county in eastern Norway, known for its forests, agriculture, and location in the traditional region of Solør.
-
B.
Balve
Balve is a small town in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known for its limestone caves and scenic Sauerland surroundings.
-
C.
Sollefteå
Sollefteå is a small town in northern Sweden known for its scenic location along the Ångerman River and its surrounding forests and hills.
-
D.
Loen
Loen is a small settlement located on Namu Atoll in the Marshall Islands, a remote Pacific island nation.
-
E.
Sagene
Sagene is a central district in Oslo, Norway, known for its historic industrial heritage along the Akerselva river and its mix of old workers’ housing and modern urban development.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Assens Triple: [Region of Southern Denmark, containsCity, Assens]
Generated description
Assens is a coastal town on the island of Funen in southern Denmark, known for its historic harbor, maritime heritage, and well-preserved old town.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Assens Target entity description: Assens is a coastal town on the island of Funen in southern Denmark, known for its historic harbor, maritime heritage, and well-preserved old town.
-
A.
Åsnes
Åsnes is a rural municipality in Innlandet county in eastern Norway, known for its forests, agriculture, and location in the traditional region of Solør.
-
B.
Balve
Balve is a small town in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known for its limestone caves and scenic Sauerland surroundings.
-
C.
Sollefteå
Sollefteå is a small town in northern Sweden known for its scenic location along the Ångerman River and its surrounding forests and hills.
-
D.
Loen
Loen is a small settlement located on Namu Atoll in the Marshall Islands, a remote Pacific island nation.
-
E.
Sagene
Sagene is a central district in Oslo, Norway, known for its historic industrial heritage along the Akerselva river and its mix of old workers’ housing and modern urban development.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca82e613e88190bf8139669bbd0d53 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f2d2c30819095075940479b75a7 |
completed | March 31, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd95665390819089c8becad018cf51 |
completed | April 1, 2026, 10 p.m. |
| NEDg | Description generation | batch_69cda62070888190b55b3f54d29e28e7 |
completed | April 1, 2026, 11:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdb21a65d88190a19dd41f95d173c8 |
completed | April 2, 2026, 12:02 a.m. |
Created at: March 30, 2026, 5:54 p.m.